Advanced stereoscopy applied to CTAO

The Cherenkov Telescope Array Observatory (CTAO) is an international observatory currently under construction, which will consist of two sites (one in the Northern Hemisphere and one in the Southern Hemisphere). It will eventually be the largest and most sensitive ground-based gamma- ray observatory. In the meantime, a small subarray composed of four Large-Sized Telescopes (LSTs) at the Northern site will begin collecting data in the coming year. In preparation, we present a stereoscopic event reconstruction using graph neural networks (GNNs) to combine information from several telescopes of this subarray. In our previous work, we explored the use of GNNs for the stereoscopic reconstruction of gamma-ray events on simulated data from the Prod5 sample and showed that GNNs provide a better stereoscopic reconstruction. We now compare this approach to the currently foreseen method that analytically combines the output of monoscopic random forests, and explore how GNNs can be used in fusion with the Random forest algorithm in order to provide a more sensitive stereoscopic system.

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Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.0549
Primary Topic
Advanced X-ray Imaging Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Advanced stereoscopy applied to CTAO

Thomas Vuillaume, Tom François, Hechmi Ben Messaoud
Advanced X-ray Imaging Techniques
article

Advanced stereoscopy applied to CTAO

Thomas Vuillaume, Tom François, Hechmi Ben Messaoud
article en

Abstract

The Cherenkov Telescope Array Observatory (CTAO) is an international observatory currently under construction, which will consist of two sites (one in the Northern Hemisphere and one in the Southern Hemisphere). It will eventually be the largest and most sensitive ground-based gamma- ray observatory. In the meantime, a small subarray composed of four Large-Sized Telescopes (LSTs) at the Northern site will begin collecting data in the coming year. In preparation, we present a stereoscopic event reconstruction using graph neural networks (GNNs) to combine information from several telescopes of this subarray. In our previous work, we explored the use of GNNs for the stereoscopic reconstruction of gamma-ray events on simulated data from the Prod5 sample and showed that GNNs provide a better stereoscopic reconstruction. We now compare this approach to the currently foreseen method that analytically combines the output of monoscopic random forests, and explore how GNNs can be used in fusion with the Random forest algorithm in order to provide a more sensitive stereoscopic system.

Centre National de la Recherche Scientifique (FR), Laboratoire d’Annecy de Physique des Particules (FR)
National Science Foundation, Sveučilište u Zagrebu, Comunidad de Madrid, Ministerio de Ciencia, Innovación y Universidades, Centres de Recerca de Catalunya, European Commission, Deutsche Forschungsgemeinschaft, Ministry of Education, Culture, Sports, Science and Technology, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Fundação de Amparo à Pesquisa do Estado de São Paulo, Ministerstvo Školství, Mládeže a Tělovýchovy, Bundesministerium für Bildung und Forschung, Generalitat de Catalunya, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Max-Planck-Gesellschaft, Narodowe Centrum Nauki, Hrvatska Zaklada za Znanost, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação Araucária, Centre National de la Recherche Scientifique, Ministerio de Ciencia e Innovación, Istituto Nazionale di Astrofisica, Sveučilište Josipa Jurja Strossmayera u Osijeku, Junta de Andalucía, Ministério da Ciência, Tecnologia, Inovações e Comunicações, Istituto Nazionale di Fisica Nucleare, Japan Society for the Promotion of Science, Consejo Superior de Investigaciones Científicas, Staatssekretariat für Bildung, Forschung und Innovation, European Regional Development Fund, Agencia Estatal de Investigación, Institut National de Physique Nucléaire et de Physique des Particules, Institute for Cosmic Ray Research, University of Tokyo
Openalex Percentile: Top 67%
Advanced X-ray Imaging Techniques
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